The use of convection-permitting models (CPMs) represents a fundamental change in how forecasters use numerical models—rather than inferring potential societal hazards from forecast environments predicted by relatively coarse convection-parameterizing models—CPMs provide direct information on specific fine-scale weather phenomena that produce hazardous weather (e.g., flash flood producing rainfall systems, tornadic thunderstorms, and squall lines with damaging wind gusts). However, effective use of CPMs requires new and innovative model diagnostics, verification, and visualization strategies. Furthermore, how to best configure CPM-based ensemble systems for operational forecasting is an open question and requires experiments examining the impact of different data assimilation methods, initial and lateral boundary conditions perturbations, and model errors. For this opportunity, a successful candidate would address these open research areas utilizing datasets generated as part of annual NOAA/Hazardous Weather Testbed (HWT) Spring Forecasting Experiments, NSSL's Warn-on-Forecast project, or their own carefully designed experiments. The candidate would have the opportunity to participate in or lead activities in the HWT experiments as part of their research.

 

key words

Severe weather prediction; Ensemble forecasting; Forecast verification; Operational forecasting; Model physics sensitivities; Hazardous weather testbed; Convective-scale predictability; Numerical weather prediction; Warn-on-Forecast;

Citizenship:  Open to U.S. citizens, permanent residents and non-U.S. citizens

Level:  Open to Postdoctoral and Senior applicants